An AI-based decision support system for predicting mental health disorders
Open accessAbstract
Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.
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AI-based tools have demonstrated remarkable capabilities in various domains, including image analysis, natural language processing and predictive modelling.
“AI-based tools have demonstrated remarkable capabilities in various domains, including image analysis, natural language processing and predictive modelling (75).”
From Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners. (2023) · cites this paper as [75]
Mobile health apps equipped with decision support systems have the potential to enhance clinical decision-making by providing evidence-based recommendations to healthcare providers.
“Mobile health apps equipped with decision support systems have the potential to enhance clinical decision-making by providing evidence-based recommendations to healthcare providers (39, 67, 75).”
From Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners. (2023) · cites this paper as [75]
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- Authors
- Tutun, S., Johnson, M. E., Ahmed, A., Albizri, A., Irgil, S., Yesilkaya, I., Ucar, E. N., Sengun, T., & Harfouche, A.
- Affiliations
- Washington University in St. Louis (US)
- Montclair State University (US)
- University of Alabama at Birmingham (US)
- Medical Park Gaziantep Hospital (TR)
- Université Paris Nanterre (FR)
- Corresponding author
- Harfouche, Antoine
- ORCID
- Venue
- Information Systems Frontiers
- Published
- 2022-05-28
- Volume
- 25
- Issue
- 3
- Pages
- 1261-1276
- ISSN
- 1387-3326 (ISSN-L)
- 1572-9419
- PMC
- PMC9142346
- OpenAlex
- W4281627170
- Cited by
- 177
- References
- 86
- Citations by year
- 2026: 40 · 2025: 62 · 2024: 60 · 2023: 15
- FWCI
- 25.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Information Systems Frontiers (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- World Health Organization
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- mental illness, automated diagnosis, decision support system, depression, bipolar disorder, schizophrenia, anxiety, clinical decision support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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